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Automated Reasoning for the Synthesis and Analysis of Biological Programs

  • Sara-Jane DunnEmail author
  • Boyan Yordanov
Chapter
Part of the Computational Biology book series (COBO, volume 30)

Abstract

Cellular decision-making arises as the output of biochemical information processing, as complex cascades of molecular interactions are triggered by input stimuli. Deciphering critical interactions and how they are organised into biological programs is a huge challenge, compounded by the difficulty of manually navigating alternative hypotheses consistent with observed behaviour. Against this backdrop, automated reasoning is a powerful methodology to tackle biological complexity and derive explanations of behaviour that are provably consistent with experimental evidence. We present a reasoning framework that permits the synthesis and analysis of a set of dynamic biological interaction networks. Employing methods based on Satisfiability Modulo Theories (SMT), we encode experimental observations as specifications of expected dynamics, and synthesise networks consistent with these constraints. Predictions of untested behaviour are generated based on all consistent models, without requiring time-consuming simulation or state space exploration, and the method can be used to identify additional components, topological ‘switches’ that allow cell state changes, and to predict gene-level dynamics. We show the reader how to utilise this reasoning framework to encode and explore rich queries for their biological system of choice.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Microsoft ResearchCambridgeUK

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